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Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israels Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture.

The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today:

Top 20 Machine Learning Startups To Watch In 2021

Augury Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise. This enables customers to adjust their maintenance and manufacturing processes based on actual machine conditions. The platform is in use with HVAC, industrial factories and commercial facilities.

Alation Alation is credited with pioneering the data catalog market and is well-respected in the financial services community for its use of A.I. to interpret and present data for analysis. Alation has also set a quick pace to evolving its platform to include data search & discovery, data governance, data stewardship, analytics and digital transformation. With its Behavioral Analysis Engine, inbuilt collaboration capabilities and open interfaces, Alation combines machine learning with human insight to successfully tackle data and metadata management challenges. More than 200 enterprises are using Alations platform today, including AbbVie, American Family Insurance, Cisco, Exelon, Finnair, Munich Re, New Balance, Pfizer, Scandinavian Airlines and U.S. Foods. Headquartered in Silicon Valley, Alation is backed by leading venture capitalists including Costanoa, Data Collective, Icon, Sapphire and Salesforce Ventures.

Algorithmia Algorithmias expertise is in machine learning operations (MLOps) and helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development and leverages existing SDLC and CI/CD practices. Over 110,000 engineers and data scientists have used Algorithmias platform to date, including the United Nations, government intelligence agencies and Fortune 500 companies.

Avora Avora is noteworthy for its augmented analytics platform, making in-depth data analysis intuitively as easy as performing web searches. The companys unique technology hides complexity, empowering non-technical users to run and share their reports easily. By eliminating the limitations of existing analytics, reducing data preparation and discovery time by 50-80% and accelerating time to insight, Avora uses ML to streamline business decision-making. Headquartered in London with offices in New York and Romania, Avora helps accelerate decision making and productivity for customers across various industries and markets, including Retail, Financial Services, Advertising, Supply Chain and Media and Entertainment.

Boast.ai Focused on helping companies in the U.S. and Canada recover their R&D costs from respective federal governments, Boast.ai enables engineers and accountants to gain tax credits using AI-based tools. Some of the tax programs Boast.ai works with include US R&D Tax Credits, Scientific Research and Experimental Development (SR&ED) and Interactive Digital Media Tax Credits (IDMTC). The startup has offices in San Francisco, Vancouver and Calgary.

ClosedLoop.ai An Austin, Texas-based startup, ClosedLoop.ai has created one of the healthcare industrys first data science platforms that streamline patient experiences while improving healthcare providers profitability. Their machine learning automation platform and a catalog of pre-built predictive and prescriptive models can be customized and extended based on a healthcare providers unique population or client base needs. Examples of their technology applications include predicting admissions/readmissions, predicting total utilization & total risk, reducing out-of-network utilization, avoiding appointment no-shows, predicting chronic disease onset or progression and improving clinical documentation and reimbursement. The Harvard Business School, through its Kraft Precision Medicine Accelerator, recently named ClosedLoop.ai as one of the fastest accelerating companies in its Real World Data Analytics Landscapes report.

Databand A Tel Aviv-based startup that provides a software platform for agile machine learning development, Databand was founded in 2018 by Evgeny Shulman, Joshua Benamram and Victor Shafran. Data engineering teams are responsible for managing a wide suite of powerful tools but lack the utilities they need to ensure their ops are running properly. Databand fills this gap with a solution that enables teams to gain a global view of their data flows, make sure pipelines complete successfully and monitor resource consumption and costs. Databand fits natively in the modern data stack, plugging seamlessly into tools like Apache Airflow, Spark, Kubernetes and various ML offerings from the major cloud providers.

DataVisor DataVisors approach to using AI for increasing fraud detection accuracy on a platform level is noteworthy. Using proprietary unsupervised machine learning algorithms, DataVisor enables organizations to detect and act on fast-evolving fraud patterns and prevent future attacks before they happen. Combining advanced analytics and an intelligence network of more than 4.2B global user accounts, DataVisor protects against financial and reputational damage across various industries, including financial services, marketplaces, e-commerce and social platforms. Theyre one of the more fascinating cybersecurity startups using AI today.

Exceed.ai What makes Exceed.ai noteworthy is how their AI-powered sales assistant platform automatically communicates the leads context and enables sales and marketing teams to scale their lead engagement and qualification efforts accordingly. Exceed.ai follows up with every lead and qualifies them quickly through two-way, automated conversations with prospects using natural language over chat and email. Sales reps are freed from performing error-prone and repetitive tasks, allowing them to focus on revenue-generating activities such as phone calls and demos with potential customers.

Indico Indico is a Boston-based startup specializing in solving the formidable challenge of how dependent businesses are on unstructured content yet lack the frameworks, systems and tools to manage it effectively. Indico provides an enterprise-ready A.I. platform that organizes unstructured content while streamlining and automating back-office tasks. Indico is noteworthy given its track record of helping organizations automate manual, labor-intensive, document-based workflows. Its breakthrough in solving these challenges is an approach known as transfer learning, which allows users to train machine learning models with orders of magnitude fewer data than required by traditional rule-based techniques. Indico enables enterprises to deploy A.I. to unstructured content challenges more effectively while eliminating many common barriers to A.I. & ML adoption.

LeadGenius LeadGenius is noteworthy for its use of AI to provide personalized and actionable B2B lead information that helps its clients attain their global revenue growth goals. LeadGeniuss worldwide team of researchers uses proprietary technologies, including AI and ML-based techniques, to deliver customized lead generation, lead enrichment and data hygiene services in the format, methods and frequency defined by the customer. Their mission is to enable B2B sales and marketing organizations to connect with their prospects via unique and personalized data sets.

Netra Netra is a Boston-based startup that began as part of MIT CSAIL research and has multiple issued and pending patents on its technology today. Netra is noteworthy for how advanced its video imagery scanning and text metadata interpretation are, ensuring safety and contextual awareness. Netras patented A.I. technology analyzes videos in real-time for contextual references to unsafe content, including deepfakes and potential cybersecurity threats.

Particle Particle is an end-to-end IoT platform that combines software including A.I., hardware and connectivity to provide a wide range of organizations, from startups to enterprises, with the framework they need to launch IoT systems and networks successfully. Particle customers include Jacuzzi, Continental Tires, Watsco, Shifted Energy, Anderson EV, Opti and others. Particle is venture-backed and has offices in San Francisco, Shenzhen, Las Vegas, Minneapolis and Boston. Particles developer community includes over 200,000 developers and engineers in more than 170 countries today.

RideVision RideVision was founded in 2018 by motorcycle enthusiasts Uri Lavi and Lior Cohen. The company is revolutionizing the motorcycle-safety industry by harnessing the strength of artificial intelligence and image-recognition technology, ultimately providing riders with a much broader awareness of their surroundings, preventing collisions and enabling bikers to ride with full confidence that they are safe. RideVisions latest round was $7 million in November of last year, bringing their total funding to $10 million in addition to a partnership with Continental AG.

Savvie Savvie is an Oslo-based startup specializing in translating large volumes of data into concrete actions that bakery and caf owners can utilize to improve their bottom line every day. In doing so, we help food businesses make the right decisions to optimize their operations and increase profitability while reducing waste at its source. Whats noteworthy about this startup is how adept they are at fine-tuning ML algorithms to provide their clients with customized recommendations and real-time insights about their food and catering businesses. Their ML-driven insights are especially valuable given how bakery and caf owners are pivoting their business models in response to the pandemic.

SECURITI.ai One of the most innovative startups in cybersecurity, combining AI and ML to secure sensitive data in multi-cloud and mixed platform environments, SECURITI.ai is a machine learning company to watch in 2021, especially if you are interested in cybersecurity. Their AI-powered platform and systems enable organizations to discover potential breach risk areas across multi-cloud, SaaS and on-premise environments, protect it and automate all private systems, networks and infrastructure functions.

SkyHive SkyHive is an artificial intelligence-based SaaS platform that aims to reskill enterprise workforces and communities. It develops and commercializes a methodology, Quantum Labor Analysis, to deliver real-time, skill-level insights into internal workforces and external labor markets, identify future and emerging skills and facilitate individual-and company-level reskilling. SkyHive is industry-agnostic and supporting enterprise and government customers globally with a mission to reduce unemployment and underemployment. Sean Hinton founded the technology company in Vancouver, British Columbia, in 2017.

Stravito Stravito is an A.I. startup thats combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable. Thor Olof Philogne and Sarah Lee founded the company in 2017, who identified an opportunity to help companies be more productive, getting greater value from their market research investments. Thor Olof Philogne and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (H.Q.), Malm and Amsterdam.

Verta.ai Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform to launch models into production.Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work define the first open-source system for managing machine learning models. Her dissertation,Infrastructure for model management and model diagnosis, proposes ModelDB, a system to track ML-based workflows provenance and performance.In August of this year, Verta received a $10 million Series A round led by Intel Capital and General Catalyst, who also led its $1.7 million seed round. For additional details on Verta.ai, please see How Startup Verta Helps Enterprises Get Machine Learning Right. The Verta MLOps platform launch webinar provides a comprehensive overview of the platform and how its been designed to streamline machine learning models into production:

V7 V7 allows vision-based A.I. systems to learn continuously from training data with minimal human supervision. The London-based startup emerged out of stealth in August 2018 to reveal V7 Darwin, an image labeling platform to create training data for computer vision projects with little or no human involvement necessary. V7 specializes in healthcare, life sciences, manufacturing, autonomous driving, agri-tech, sporting clients like Merck, GE Healthcare and Toyota. V7 Darwin launched at CVPR 2019 in Long Beach, CA. Within its first year, it has semi-automatically annotated over 1,000 image and video segmentation datasets. V7 Neurons is a series of pre-trained image recognition applications for industry use. The following video explains how V7 Darwin works:

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The Top 20 Machine Learning Startups To Watch In 2021 - Enterprise Irregulars

Bob van Luijt's career in technology started at age 15, building websites to help people sell toothbrushes online. Not many 15 year-olds do that. Apparently, this gave van Luijt enough of a head start to arrive at the confluence of technology trends today.

Van Luijt went on to study arts but ended up working full time in technology anyway. In 2015, whenGoogle introduced its RankBrain algorithm, the quality of search results jumped up. It was a watershed moment, as it introduced machine learning in search. A few people noticed, including van Luijt, who saw a business opportunity and decided to bring this to the masses.

ZDNetconnected with van Luijt to find out more.

Does Google's RankBrain machine learning improve search results for users? People were wondering at the time RankBrain was introduced. As ZDNet's own Eileen Brown noted:Yes, and results delivered by RankBrain will get better as it learns what we are trying to ask of it.

For van Luijt, this was an "Aha" moment. Like everyone else working in technology, he had to deal with lots of unstructured data. In his words, relating data is a problem.Data integration is hard to do, even for structured data. When you have unstructured data from different sources, it becomes extremely challenging.

Van Luijt read up on RankBrain and figured it uses word vectorization to infer relations in the queries and then try to present results.Vectors are how machine learning models understandthe world. Where people see images, for example, machine learning models see image representations, in the form of vectors.

The introduction of Google's RankBrain algorithm was a watershed moment for search, as it introduced machine learning to search. Image: Search Engine Journal

A vector is a very long list of numbers, which can be thought of as coordinates in a geometrical space. Three-dimensional vectors -- i.e. vectors of the form (X, Y, Z) -- correspond to a space humans are familiar with. But multi-dimensional vectors also exist, and this complicates things:

"There are many dimensions, but to paint a mental picture, you can say there's just three dimensions. The problem now is, it's great that you can use a vector to recognize a pattern in a photo and then say, yes, it's a cat, or no, it's not a cat. But then, what if you want to do that for one hundred thousand photos or for a million photos? Then you need a different solution, you need to have a way to look into the space and find similar things."

This is what Google did with RankBrain for text. Van Luijt was intrigued. He started experimenting with Natural Language Processing (NLP) models. He even got to ask Google's people directly: Were they going to build a B2B search engine solution? Since their reply was "no," he set out to do that withWeaviate.

NLP machine learning models output vectors: They place individual words in a vector space. The idea behind Weaviate was: What if we take a document -- an email, a product, a post, whatever -- look at all the individual words that describe it and calculate a vector for those words.

This will be where the document sits in the vector space. And then, if you ask, for example: What publications are most related to fashion? The search engine should look into the vector space, and find publications like Vogue, as being close to "fashion" in this space.

This is at the core of what Weaviate does. In addition,data in Weaviate are stored in a graph format. When nodes in the graph are located, users can traverse further and find other nodes in the graph.

Weaviate uses vectors to search for documents in spaces comprising of many dimensions. (Image: Weaviate)

It's not that it isn't possible to store vectors in traditional databases. It is, and people do that. But after a certain point, it becomes impractical. Besides performance, complexity is also a barrier. For example, van Luijt mentioned, in most cases, people are not privy to the details of how vectorization happens.

Weaviate comes with a number of built-in vectorizers. Some are general-purpose, some are tailored to specific domains such as cybersecurity orhealthcare. A modular structure enables people to plugin their own vectorizers, too.

Weaviate also works with popular machine learning frameworks such asPyTorchor TensorFlow. However, there is a catch: At this time, if you train your model, or use one provided by Weaviate, you're stuck with it.

If a model changes in a way that influences the way it generates vectors, Weaviate would have to re-index its data to work. This is not currently supported. Van Luijt mentioned it was not required in their current use cases, but they are looking into ways of supporting that.

As a startup,SeMI Technologies, the company van Luijt founded around Weaviate, is navigating the market for traction. Currently, the retail andFMCGindustry is working well for them, withMetro AGbeing a prominent use case.

The challenge that Metro had was how to find new opportunities in the market. Weaviate helped them do that by combining data from theirCRMandOpen Street Maps. If a location where a business exists could not be associated with a customer in the CRM, that indicated an opportunity.

Across industries, van Luijt noted, the problem is always the same at the root level: unstructured data needs to be related to something internally structured. Graphs are well-known for helping leverage connections. But it turns out that even the inability to find connections can generate business value, as the Metro use case exemplifies.

Van Luijt is a firm believer in the value of graphs for leveraging connections -- or lack thereof. Stacking up data in data warehouses and data lakes andlakehousesand whatnot does have value. But, to get value from connections in the data, it'sthe graph model that makes the most sense, he noted.

Then, the question becomes: How are we going to get people access to this? To give people a lot of capabilities so they can do "a tremendous amount of stuff," agraph query languagelike SPARQL may make sense, van Luijt said.

GraphQL's meteoric rise among developers has attracted interest in using it as an access layer for databases, too. Image: Apollo

But if you want to make it simple for people to access graphs so they have a very short learning curve, GraphQL becomes interesting, he went on to add: "Most developers who are unfamiliar with graph technology, if they see SPARQL, they start sweating and they get nervous. If they see GraphQL, they go like, 'Hey, I understand this. This makes sense.'"

There's anotherupside to GraphQL: the community around it. There are many libraries available, and because Weaviate uses GraphQL, these libraries can be used as well. Van Luijt described the decision to use GraphQL as auser experience (UX)decision -- the UX to access an API should be smooth.

Weaviate also supports the notion of schemas. When an instance starts running, the API endpoint becomes available, and the first thing users need to do is to create a class property schema. It can be as simple or as complex as it needs to, and existing schemas can also be imported.

Van Luijt has very pragmatic views when it comes to the limitations of vectors, as well as to the use of open source. Toquote Gary MarcusandRay Mooney before him, "You can't cram the meaning of a whole $&!#* sentence into a single $!#&* vector".

That much is true, but does it matter if you can get practical results out of using vectors? Not much, argues van Luijt. The problem Weaviate is trying to solve is finding things. So, if the similarity search does a good job in finding things using vectors, that's good enough. The idea, he went on to add, is to turn vectorization-based search from a data science problem into an engineering problem.

The same pragmatic approach is taken when it comes to open source. There are many reasons why people choose to go with open source. For Weaviate, open source, or ratheropen core, was chosen as a mechanism for transparency towards customers and users.

Perhaps surprisingly, van Luijt noted Weaviate is not necessarily looking for contributors. That would be nice to have, but the main purpose being open source serves is enabling audits. When clients ask their experts to audit Weaviate,being open source enables this.

Weaviate is available both as Software-as-a-Service and on-premises. Counter to conventional wisdom, it seems most Weaviate users are interested in on-premise deployments.

In practice, however, this oftentimes means their own project in one of the major cloud providers, with services from the Weaviate team. As the team and the product scale-up, a shift toward the self-service model may be called for.

Disclosure: SeMI Technologies has worked with the author as a client.

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Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL - ZDNet

A startup is employing machine learning to process aerial imagery and remotely analyze insurance risks to properties around the country.

Why it matters: The combination of AI and aerial imagery from satellites and even stratospheric balloons can help insurers quickly judge property risks without an in-person visit, saving money and time.

How it works: Arturo's AI model can identify potentially risky characteristics of a property like roof tiles in need of repair or a pool that lacks a fence and estimate the likelihood of an insurable accident in the future.

Background: Arturo's business model is a combination of two major technological trends: the ever-increasing growth of aerial imagery that can capture detailed pictures of the ground and the power of machine learning.

The big picture: Insurance might seem like the blandest of businesses, but since its origins hundreds of years ago, the field has focused on using available data to try to predict the future which happens to be precisely what machine learning is good at.

The catch: Given that insurance essentially exists as a shared hedge against uncertainty, the better insurance companies get at predicting the future, the harder it might be for some properties or people with higher risk profiles to get protection.

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Using "eyes in the sky" and AI to remotely rate insurance risks - Axios